VOSViewer co-authorship mapping is a powerful tool typically used for analyzing research collaboration. Users provide publication data and VOSViewer produces a map where authors are plotted on a 2-dimensional map based on how often they are in the author lists of the same publication. In this presentation, I propose a series of tweaks to the input data that can leverage co-authorship maps to support leadership selection based on how often candidates co-author papers with their institutional peers and some of the attributes of these papers. I will suggest how best to interpret the resulting maps and address the major assumptions that must be kept in mind when using these maps for this purpose. Lastly, I will discuss the lessons learned when we offered such maps to support a series of internal leadership selections for Canada’s largest research hospital. Presented at the 2024 Research Analytics Summit in Albuquerque, NM
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Relaxation of Wave Maps Exterior to a Ball to Harmonic Maps for All Data
- Award ID(s):
- 1160817
- PAR ID:
- 10331705
- Date Published:
- Journal Name:
- Geometric and Functional Analysis
- Volume:
- 24
- Issue:
- 2
- ISSN:
- 1016-443X
- Page Range / eLocation ID:
- 610 to 647
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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